Biotechnological analysis and examination are often based on the measurement of various chemical analytes in the composition of a biological matrix such as blood, interstitial fluid, or living tissue. Such measurements may be used to evaluate a patient's state of health and to determine what, if any, treatment is necessary. For example, the frequent monitoring of blood glucose levels in diabetic persons with glucose meters is often necessary to allow such persons to manage the diabetes mellitus disease, by taking insulin injections or oral drugs to lower blood glucose when required. Intensive treatment based on frequent blood glucose measurements can significantly reduce the incidence of blindness, kidney loss, and other complications associated with diabetes.
Most home-based glucose measurement systems require the patient to invasively collect a blood sample, by pricking his or her finger, placing the sample on an appropriate test strip, and then testing the sample in an optical glucose meter. For millions of diabetics around the world, the use of lancets or other sharp instruments to draw blood for monitoring their insulin levels is a painful process, and one that often builds up calluses on fingers, making the collection of blood even more difficult. This invasive procedure may be especially difficult to perform on children and therefore particularly trying on parents. Furthermore, the test strips required for each blood sample are generally not reusable, and when multiple measurements are taken each day, amount to significant costs from the patient's point of view. Thus, despite the fact that a large number of diabetics should take several measurements throughout each day (for some individuals, physicians recommend testing glucose levels from 4 to 7 times daily), due to the pain, cost, and inconvenience involved, many diabetics do not monitor their glucose levels frequently enough.
Spectroscopy is based on the analysis of how incident radiation interacts with the vibrational and rotational states of molecules which are of analytical interest. Spectroscopic measurement techniques have gained increased popularity because of the ability to provide fast and non-invasive measurements of concentrations of different chemicals or analytes. For the reasons indicated above, this is particularly desirable for home based glucose meters. Spectrophotometry is a type of spectroscopy commonly used to quantitatively measure concentrations of analytes based on spectral energy distribution in the absorption spectrum of a sample solution or medium. In spectrophotometry, the energy distribution is typically analyzed within a range of the visible, ultraviolet, infrared, or near-infrared spectra. For example, near-infrared radiation (NIR) is electromagnetic radiation having a wavelength of between about 0.75 and 2.5 micrometers (i.e. from 150 to 400 THz). Near-infrared spectrophotometry generally uses instruments with quartz prisms in monochromators and with lead sulfide photoconductor cells as detectors to observe absorption bands, and NIR spectrophotometry is increasingly being used to measure in vivo analytes such as glucose, total hemoglobin, albumin cholesterol, and ethanol.
Non-invasive, spectrophotometric measurement of glucose in human beings is performed by focusing an incident radiation source (or sources) on a specific part of the body and detecting the spectral distribution of the radiation transmitted therethrough. The absorbance of light from the incident radiation is due to the chemical components within that body part including water, fat, protein, hemoglobin, melanin, and glucose. One difficulty with glucose measurement spectral analysis, is the spectral overlap between glucose and other chemicals found in blood, often in much greater quantities than glucose. In addition, the thickness, color, and structure of the skin, bones, and blood through which the incident radiation passes will affect the transmission (or reflection/absorption) thereof. Furthermore, the concentration of analytes can vary with changes in activity level, diet, hormone fluctuations, and other factors. Glucose concentration measurements are also particularly susceptible to variations in physical and chemical conditions including temperature, pressure, humidity, and skin hydration. As a result, to perform a reliable non-invasive glucose prediction, NIR spectral measurements should be performed through a vascular equilibrated region of the body, and a NIR glucose spectrophotometer must be carefully designed so that the quality of raw spectral information from an NIR glucose meter is high. See generally Waynant and Chenault, “Overview of Non-Invasive Optical Glucose Monitoring Techniques”, IEEE LEOS Newsletter, vol. 12, no. 2 (April 1998); and. Burmeister and Arnold, “Spectroscopic Considerations for Noninvasive Blood Glucose Measurements with Near Infrared Spectroscopy”, IEEE LEOS Newsletter, vol. 12, no. 2 (April 1998).
Near-infrared glucose measurements are generally suitable for tissue depths ranging from about 1 mm to 10 cm, and are often performed through a patient's finger tip, although other areas of the body (for example the web between two fingers, an ear lobe, or the upper lip) can also be used. The sample thickness is an important experimental parameter because a greater thickness increases the amount of absorption and thereby lowers the minimum limits for detection, whereas because less incident radiation successfully traverses through a thicker sample (i.e. without being absorbed) effectively increasing the spectral noise: see Burmeister and Arnold, supra.
In addition to the difficulties of obtaining accurate spectral measurements with NIR spectrophotometry, a more significant difficulty associated with such measurements has been the need to calibrate such an instrument so that it may be used by various different individuals, whose analyte concentrations and variability, for example, may differ substantially. Calibration of spectrophotometers (and of analytical instruments in general) is necessary to ensure the accuracy of measurements performed by such devices.
Two approaches to calibrate and predict analyte (e.g. glucose) levels have been used in the prior art. In the first approach, a thorough calibration process is performed separately on each individual with whom the instrument is to be used. This individual calibration process requires taking a number of blood samples over a period of time from the individual, and obtaining reference glucose concentration measurements from these samples. A number of corresponding non-invasive spectroscopic measurements are taken concurrently, and calibration regression analysis is then performed to correlate, e.g. through linear regression analysis, the non-invasive measurements with the reference measurements. In this manner, the spectroscopic device or spectrophotometer is custom calibrated to the specific individual. A major disadvantage of this approach, however, is that the calibration model is limited to data from that particular individual which may have an insufficient amount of information regarding variation of spectra overlapping (interfering with) those of the analyte of interest. It is important to be able to characterize such spectra to enable highly accurate analyte predictions. To alleviate this problem, the custom calibration process may have to be carried out frequently, even on a daily basis.
An alternative calibration approach requires that a universal or general calibration algorithm be developed. This approach is based on the notion that by making a large number of calibration measurements (as in the first approach) a single calibration model that sufficiently accounts for all individual variability of all overlapping or interfering analytes can be calculated. Chemometrics, the application of mathematical, statistical and formal logic methods to chemistry, is generally used to process and compute the spectral intensity data and to produce a calibration model: see Small and Arnold, “Data Handling Issues for Near-Infrared Glucose Measurements”, IEEE IEOS Newsletter, vol. 12, no. 2 (April 1998); and Shaffer, Small, and Arnold, “Genetic Algorithm-Based Protocol for Coupling Digital Filtering and Partial Least-Squares Regression; Application to the Near-Infrared Analysis of Glucose in Biological Matrices,” Anal. Chem. 68, 2663-2675 (1996). However, for measurements of glucose, and other analytes which vary distinctly and differently from one individual sample to another, a single universal calibration algorithm is often ineffective and can result in significant, and sometimes dangerous, erroneous predictions of analyte concentration levels.
Thus, while NIR measurements provide a non-invasive, fast, painless, and convenient technique to monitor glucose levels, correlation and clinical interpretation of spectral measurements to obtain the true glucose levels is crucial for proper therapy and disease management. Proper calibration of instruments for different patient populations (which will vary in ethnicity, age, weight, and so on) is crucial in obtaining accurate glucose prediction models. Furthermore, careful validation and testing of the non-invasive results and the glucose prediction equation is needed to determine if the glucose correlation is consistent in all clinically important conditions and for all, or at least most, types of patients.